Fix layout autotune in windows ci (#45751)

paddle/fluid/eager/api/manual/eager_manual/forwards/conv2d_fwd_function.cc

@@ -86,7 +86,8 @@ paddle::experimental::Tensor conv2d_dygraph_function(
     auto transformer = egr::EagerLayoutAutotune<std::string>(
         op_name, tensors_vector, &data_format);
     auto NEW_input = transformer->TransInTensor("input", input);
-
+    bool is_enable_tune =
+        paddle::imperative::LayoutAutoTune::Instance().UseLayoutAutoTune();
     paddle::imperative::LayoutAutoTune::Instance().DisableLayoutAutoTune();
     auto out = conv2d_dygraph_function(NEW_input,
                                        filter,
@@ -100,7 +101,9 @@ paddle::experimental::Tensor conv2d_dygraph_function(
                                        workspace_size_MB,
                                        exhaustive_search);
     transformer->SetOutTensorLayout(&out);
-    paddle::imperative::LayoutAutoTune::Instance().EnableLayoutAutoTune();
+    if (is_enable_tune) {
+      paddle::imperative::LayoutAutoTune::Instance().EnableLayoutAutoTune();
+    }
     // Returns
     return out;
   }

python/paddle/fluid/tests/unittests/test_layout_autotune.py

@@ -46,6 +46,9 @@ class SimpleNet(paddle.nn.Layer):
 
 class LayoutAutoTune(unittest.TestCase):
 
+    def setUp(self):
+        self.use_autoune()
+
     def use_autoune(self):
         if paddle.is_compiled_with_cuda():
             paddle.incubate.autotune.set_config(
@@ -85,16 +88,18 @@ class LayoutAutoTune(unittest.TestCase):
     def test_enable_autotune(self):
         if self.use_autoune():
             conv_out, predict = self.train(data_format="NCHW")
-            self.assertEqual(conv_out.shape, [1, 14, 14, 8])
-            self.assertEqual(predict.shape, [1, 2])
+            if paddle.fluid.core.use_layout_autotune():
+                self.assertEqual(conv_out.shape, [1, 14, 14, 8])
+                self.assertEqual(predict.shape, [1, 2])
+            else:
+                self.assertEqual(conv_out.shape, [1, 8, 14, 14])
+                self.assertEqual(predict.shape, [1, 2])
         else:
             conv_out, predict = self.train(data_format="NCHW")
             self.assertEqual(conv_out.shape, [1, 8, 14, 14])
             self.assertEqual(predict.shape, [1, 2])
 
     def test_transpose_op_transposer(self):
-        if not self.use_autoune():
-            return
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
         data = paddle.rand([1, 3, 16, 14])
         label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
@@ -112,12 +117,14 @@ class LayoutAutoTune(unittest.TestCase):
         scaled.backward()
         scaler.minimize(optimizer, scaled)
 
-        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-        self.assertEqual(out.shape, [1, 12, 8, 14])
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [1, 12, 8, 14])
+        else:
+            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [1, 12, 8, 14])
 
     def test_flatten_op_transposer(self):
-        if not self.use_autoune():
-            return
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
         flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
         data = paddle.rand([1, 3, 16, 14])
@@ -129,25 +136,42 @@ class LayoutAutoTune(unittest.TestCase):
             # because it flatten the C and H dimensions.
             out = flatten(conv_out)
 
-        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-        self.assertEqual(out.shape, [1, 112, 12])
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [1, 112, 12])
+        else:
+            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [1, 112, 12])
 
     def test_argmax_op_transposer_keep_dims(self):
-        if not self.use_autoune():
-            return
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
         data = paddle.rand([1, 3, 16, 14])
         with paddle.amp.auto_cast(level="O2"):
             conv_out = conv(data)
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.argmax(conv_out, axis=1, keepdim=True)
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [1, 14, 12, 1])
+        else:
+            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [1, 1, 14, 12])
 
-        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-        self.assertEqual(out.shape, [1, 14, 12, 1])
+    def test_argmax_op_transposer_ff(self):
+        conv = paddle.nn.Conv2D(3, 8, (3, 3))
+        data = paddle.rand([1, 3, 16, 14])
+        with paddle.amp.auto_cast(level="O2"):
+            conv_out = conv(data)
+            # conv_out.shape = [1, 14, 12, 8] with NHWC
+            out = paddle.argmax(conv_out)
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [1])
+        else:
+            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [1])
 
-    def test_argmax_op_transposer(self):
-        if not self.use_autoune():
-            return
+    def test_argmax_op_transposer_t(self):
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
         data = paddle.rand([1, 3, 16, 14])
         with paddle.amp.auto_cast(level="O2"):
@@ -155,12 +179,14 @@ class LayoutAutoTune(unittest.TestCase):
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.argmax(conv_out)
 
-        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-        self.assertEqual(out.shape, [1])
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [1])
+        else:
+            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [1])
 
     def test_concat_op_transposer(self):
-        if not self.use_autoune():
-            return
         in1 = paddle.rand([1, 8, 14, 12])
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
         data = paddle.rand([1, 3, 16, 14])
@@ -169,12 +195,14 @@ class LayoutAutoTune(unittest.TestCase):
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.concat(x=[conv_out, in1], axis=0)
 
-        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
-        self.assertEqual(out.shape, [2, 8, 14, 12])
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [2, 8, 14, 12])
+        else:
+            self.assertEqual(conv_out.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [2, 8, 14, 12])
 
     def test_concat_op_no_transposer(self):
-        if not self.use_autoune():
-            return
         conv = paddle.nn.Conv2D(3, 8, (3, 3))
         data1 = paddle.rand([1, 3, 16, 14])
         data2 = paddle.rand([1, 3, 16, 14])
@@ -184,8 +212,12 @@ class LayoutAutoTune(unittest.TestCase):
             # conv_out.shape = [1, 14, 12, 8] with NHWC
             out = paddle.concat(x=[conv_out1, conv_out2], axis=0)
 
-        self.assertEqual(conv_out1.shape, [1, 14, 12, 8])
-        self.assertEqual(out.shape, [2, 14, 12, 8])
+        if paddle.fluid.core.use_layout_autotune():
+            self.assertEqual(conv_out1.shape, [1, 14, 12, 8])
+            self.assertEqual(out.shape, [2, 14, 12, 8])
+        else:
+            self.assertEqual(conv_out1.shape, [1, 8, 14, 12])
+            self.assertEqual(out.shape, [2, 8, 14, 12])
 
 
 class TestAutoTuneAPI(unittest.TestCase):